Language models (LMs) can be directed to perform target tasks by using
labeled examples or natural language prompts. But selecting examples or writing
prompts for can be challenging--especially in tasks that involve unusual edge
cases, demand precise articulation of nebulous preferences, or require an
accurate mental model of LM behavior. We propose to use *LMs themselves* to
guide the task specification process. In this paper, we introduce **Generative
Active Task Elicitation (GATE)**: a learning framework in which models elicit
and infer intended behavior through free-form, language-based interaction with
users. We study GATE in three domains: email validation, content
recommendation, and moral reasoning. In preregistered experiments, we show that
LMs prompted to perform GATE (e.g., by generating open-ended questions or
synthesizing informative edge cases) elicit responses that are often more
informative than user-written prompts or labels. Users report that interactive
task elicitation requires less effort than prompting or example labeling and
surfaces novel considerations not initially anticipated by users. Our findings
suggest that LM-driven elicitation can be a powerful tool for aligning models
to complex human preferences and values.

使用语言模型自身来引导任务规范化的学习框架，通过与用户进行自由形式的、基于语言的交互来提取和推断预期行为，在电子邮件验证、内容推荐和道德推理三个领域的实验中，我们展示了通过生成开放式问题或综合信息临界案例等方式激发语言模型表现的结果通常比用户编写的提示或标签更具信息量，用户反馈认为交互式任务引导所需的工作量较小，并能提供用户一开始未曾预见的新颖考虑。研究结果表明，基于语言模型的任务引导可以成为将模型与复杂人类偏好和价值观相一致的强大工具。